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Fig.1(a) shows a typical DBN for deep feature learning from hyperspectral image.  In DBN, the output of previous RBM is used as input data for a next RBM
Fig. 2. Pavia University data set. (a) Original image produced by the mixture of three bands
Fig. 3. Example results of the learned weight parameters over the Pavia University data set: (a) is the learned weight parameters of the second layer of original DBN, (b) and (c) are the weights diversified by D-DBN-P and D-DBN-PF respectively
Table 3 Classification accuracies of different methods.

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